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Principal Machine Learning Engineer Jobs in California

SAP

Principal Machine Learning Engineer

Palo Alto, CA · On-site

$158K - $212K/yr

As a Machine Learning Engineer Expert you will be responsible for guiding the team, providing direction and designs and support while also being a hands on developer. The Role: · Work closely with ...

SAP

Principal Machine Learning Engineer

Palo Alto, CA · On-site

$155K - $207K/yr

As a Machine Learning Engineer Expert you will be responsible for guiding the team, providing direction and designs and support while also being a hands on developer. The Role: • Work closely with ...

Cisco

Principal Machine Learning Engineer

Palo Alto, CA

$291K - $369K/yr

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

Cisco

Principal Machine Learning Engineer

San Mateo, CA

$291K - $369K/yr

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

Cisco

Principal Machine Learning Engineer

Los Altos, CA

$291K - $369K/yr

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

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All JobsPrincipal Machine Learning Engineer JobsPrincipal Machine Learning Engineer Jobs in California

Principal Machine Learning Engineer information

See California salary details

$73K

$145.3K

$209.7K

How much do principal machine learning engineer jobs pay per year?

As of Jul 13, 2026, the average yearly pay for principal machine learning engineer in California is $145,292.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,900.00 and $170,700.00 per year, depending on experience, location, and employer.

What types of projects and responsibilities can a Principal Machine Learning Engineer typically expect in this role?

Principal Machine Learning Engineers are often tasked with leading the design, development, and deployment of large-scale machine learning models and systems that address key business challenges. In this role, you will collaborate closely with data scientists, engineers, and product managers to define project requirements, architect solutions, and ensure high-quality delivery. You may also guide research initiatives, oversee code and model reviews, and mentor junior engineers, helping to shape the technical direction of the team. Typical responsibilities can range from prototyping and optimizing algorithms to ensuring models are scalable, reliable, and aligned with organizational goals.

What are the key skills and qualifications needed to thrive in the Principal Machine Learning Engineer position, and why are they important?

To thrive as a Principal Machine Learning Engineer, you need advanced expertise in machine learning algorithms, statistical analysis, software engineering, and a strong background in computer science or related fields, often supported by a master's or PhD degree. Familiarity with tools such as Python, TensorFlow, PyTorch, cloud platforms (AWS, GCP, Azure), and relevant certifications strengthens technical capability. Leadership, strategic thinking, effective communication, and mentorship are vital soft skills for guiding teams and collaborating across departments. These competencies are essential for driving innovation, ensuring technical excellence, and influencing organizational AI initiatives.

Will MLE be replaced by AI?

Principal Machine Learning Engineers design, develop, and oversee AI and machine learning systems, and their roles involve understanding complex algorithms, data management, and model deployment. While AI automates certain tasks, MLE roles focus on building and maintaining AI infrastructure, which requires human expertise, critical thinking, and ongoing innovation that AI cannot fully replace. The role is expected to evolve alongside advancements in AI technology but remains essential for guiding AI development and ensuring ethical, effective implementation.

What does a Principal Machine Learning Engineer do?

A Principal Machine Learning Engineer leads the design, development, and deployment of machine learning models and systems. They set technical strategy, mentor engineers, and collaborate with cross-functional teams to solve complex AI challenges. Their role often includes researching new algorithms, optimizing model performance, and ensuring scalability in production environments. Additionally, they work closely with data scientists, software engineers, and product managers to align ML initiatives with business objectives.

How much do principal AI engineers make?

Principal AI engineers typically earn between $130,000 and $200,000 annually, with salaries varying based on experience, location, and industry. They often have advanced skills in machine learning, deep learning, and data science, and may receive bonuses or stock options as part of compensation packages.

What engineers make $300,000 a year?

Principal Machine Learning Engineers and senior data scientists in the tech industry often earn $300,000 or more annually, especially with extensive experience, advanced skills in deep learning and AI, and working at large technology companies or startups with competitive compensation packages. High salaries may also include bonuses, stock options, and other benefits.

What engineer makes $500,000 a year?

A Principal Machine Learning Engineer can earn $500,000 or more annually, especially with extensive experience, advanced skills in deep learning and data science, and working at large tech companies or in high-demand industries. Compensation often includes base salary, bonuses, and stock options, reflecting their seniority and expertise.
What are popular job titles related to Principal Machine Learning Engineer jobs in California? For Principal Machine Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Principal Machine Learning Engineer jobs in California look for? The top searched job categories for Principal Machine Learning Engineer jobs in California are:
Principal Machine Learning Engineer jobs near you
Infographic showing various Principal Machine Learning Engineer job openings in California as of July 2026, with employment types broken down into 92% Full Time, 4% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $145,292 per year, or $69.9 per hour.

Principal, Machine Learning Engineer

Lila Sciences

San Francisco, CA • On-site

$252K - $374K/yr

Full-time

Medical, Dental, Vision, Life

Re-posted 15 days ago

Job description

Your Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), ML engineers build and operate the systems that turn generative models and reasoning frameworks into production capabilities powering automated scientific discovery across Lila's life science domains.
We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind models spanning biological sequence design, molecular structure prediction, antibody engineering, and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine.
This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational-experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities.
What You'll Be Building
  • Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data, including distributed training across GPU clusters
  • Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows
  • Architect ML infrastructure that supports rapid iteration across sequence design, structure prediction, and multimodal scientific reasoning workloads
  • Drive the engineering side of Lila's "Lab-in-the-Loop" lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows
  • Define and advance ML engineering standards, tooling, and best practices across the AI organization
  • Collaborate with AI scientists to translate research prototypes into robust, scalable production systems, bridging the research-to-deployment gap

What You'll Need to Succeed
  • Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)
  • 10+ years of hands-on experience building and operating production ML systems at scale
  • Deep expertise in distributed training infrastructure, including experience with large-scale GPU clusters (AWS, GCP, or on-prem)
  • Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices
  • Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance
  • Demonstrated ability to drive technical direction for ML infrastructure independently, from architecture through implementation
  • Track record of cross-functional collaboration with research scientists, translating between ML methodology and engineering execution

Bonus Points For
  • Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data
  • Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows
  • Contributions to open-source ML tools, frameworks, or infrastructure projects
  • Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, or nucleic acid design)
  • Experience with model evaluation frameworks for scientific applications where ground truth is sparse or delayed

Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$252,000-$374,000 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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